Multiple View Image Denoising

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A pinhole camera (large depth of field) image capture is essential in many computer
vision applications such as Simultaneous Localization and Mapping, 3D reconstruction,
video surveillance. For these applications obtaining a set of clean images(less noise, less
blur) is important. In this thesis, we propose a new approach to acquiring pinhole images
using many pinhole cameras. The cameras can be distributed spatially to monitor a
common scene, or compactly assembled as a camera array. Each camera uses a small
aperture and short exposure to ensure minimal optical defocus and motion blur. Under
such camera settings, the incoming light is very weak and the images are extremely
noisy. We cast pinhole imaging as a denoising problem and seek to restore all the pinhole
images by jointly removing noise in different viewpoints. Our Multi-view denoising can
be used as a prior to the applications mentioned above. Our algorithm takes noisy images
taken from different viewpoints as input and groups similar patches in the input images
using depth estimation. We model intensity-dependent noise in low- light conditions and
use the principal component analysis and tensor analysis to remove such noise. The
dimensionalities for both PCA and tensor analysis are automatically computed in a way
that is adaptive to the complexity of image structures in the patches. Our method is
based on a probabilistic formulation that marginalizes depth maps as hidden variables
and therefore does not require perfect depth estimation. We validate our algorithm on
both synthetic and real images with different content. Our algorithm compares favorably
against several state-of-the-art denoising algorithms.